




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_  						=       {
    "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      UpperCAmelCase_   (     __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =task_cls()
    SCREAMING_SNAKE_CASE__       =asdict(__UpperCamelCase     )
    SCREAMING_SNAKE_CASE__       =task_template_from_dict(__UpperCamelCase     )
    assert task == reloaded
class 				__a :


     """simple docstring"""


     def   __A    (     self      :			int						)		->       Any:


         '''simple docstring'''





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




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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =Features({"""text""": Value("""string"""						)}						)
         SCREAMING_SNAKE_CASE__       ={"""text_column""": """input_text"""}
         SCREAMING_SNAKE_CASE__       =LanguageModeling.from_dict(_UpperCamelCase						)
         assert "language-modeling" == task.task
         assert input_schema == task.input_schema






class 				__a (			__lowerCamelCase					):


     """simple docstring"""


     def   __A    (     self      :			Dict						)		->       Optional[int]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =sorted(["""pos""", """neg"""]						)




     def   __A    (     self      :			Optional[Any]						)		->       Dict:


         '''simple docstring'''





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




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


         '''simple docstring'''





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




     def   __A    (     self      :			int						)		->       List[str]:


         '''simple docstring'''





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






class 				__a (			__lowerCamelCase					):


     """simple docstring"""


     def   __A    (     self      :			List[Any]						)		->       str:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =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   __A    (     self      :			List[str]						)		->       Optional[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =Features({"""question""": Value("""string"""						), """context""": Value("""string"""						)}						)
         SCREAMING_SNAKE_CASE__       =Features(
             {
                 """answers""": Sequence(
                     {
                         """text""": Value("""string"""						),
                         """answer_start""": Value("""int32"""						),
                     }						)
             }						)
         SCREAMING_SNAKE_CASE__       ={
             """context_column""": """input_input_context""",
             """question_column""": """input_question""",
             """answers_column""": """input_answers""",
         }
         SCREAMING_SNAKE_CASE__       =QuestionAnsweringExtractive.from_dict(_UpperCamelCase						)
         self.assertEqual("""question-answering-extractive"""    ,task.task						)
         self.assertEqual(_UpperCamelCase    ,task.input_schema						)
         self.assertEqual(_UpperCamelCase    ,task.label_schema						)






class 				__a (			__lowerCamelCase					):


     """simple docstring"""


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


         '''simple docstring'''





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




     def   __A    (     self      :			str						)		->       Any:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =Features({"""text""": Value("""string"""						)}						)
         SCREAMING_SNAKE_CASE__       =Features({"""summary""": Value("""string"""						)}						)
         SCREAMING_SNAKE_CASE__       ={"""text_column""": """input_text""", """summary_column""": """input_summary"""}
         SCREAMING_SNAKE_CASE__       =Summarization.from_dict(_UpperCamelCase						)
         self.assertEqual("""summarization"""    ,task.task						)
         self.assertEqual(_UpperCamelCase    ,task.input_schema						)
         self.assertEqual(_UpperCamelCase    ,task.label_schema						)






class 				__a (			__lowerCamelCase					):


     """simple docstring"""


     def   __A    (     self      :			int						)		->       Optional[Any]:


         '''simple docstring'''





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




     def   __A    (     self      :			Optional[Any]						)		->       Dict:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =Features({"""audio""": Audio()}						)
         SCREAMING_SNAKE_CASE__       =Features({"""transcription""": Value("""string"""						)}						)
         SCREAMING_SNAKE_CASE__       ={
             """audio_column""": """input_audio""",
             """transcription_column""": """input_transcription""",
         }
         SCREAMING_SNAKE_CASE__       =AutomaticSpeechRecognition.from_dict(_UpperCamelCase						)
         self.assertEqual("""automatic-speech-recognition"""    ,task.task						)
         self.assertEqual(_UpperCamelCase    ,task.input_schema						)
         self.assertEqual(_UpperCamelCase    ,task.label_schema						)






class 				__a (			__lowerCamelCase					):


     """simple docstring"""


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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =sorted(["""pos""", """neg"""]						)




     def   __A    (     self      :			int						)		->       Dict:


         '''simple docstring'''





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




     def   __A    (     self      :			Any						)		->       Dict:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =Features({"""audio""": Audio()}						)
         SCREAMING_SNAKE_CASE__       =Features({"""labels""": ClassLabel}						)
         SCREAMING_SNAKE_CASE__       ={
             """audio_column""": """input_image""",
             """label_column""": """input_label""",
         }
         SCREAMING_SNAKE_CASE__       =AudioClassification.from_dict(_UpperCamelCase						)
         self.assertEqual("""audio-classification"""    ,task.task						)
         self.assertEqual(_UpperCamelCase    ,task.input_schema						)
         self.assertEqual(_UpperCamelCase    ,task.label_schema						)




     def   __A    (     self      :			str						)		->       int:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =AudioClassification(audio_column="""input_audio"""    ,label_column="""input_label"""						)
         self.assertEqual(task.label_schema["""labels"""]    ,_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =task.align_with_features(Features({"""input_label""": ClassLabel(names=self.labels						)}						)						)
         self.assertEqual(task.label_schema["""labels"""]    ,ClassLabel(names=self.labels						)						)






class 				__a (			__lowerCamelCase					):


     """simple docstring"""


     def   __A    (     self      :			Tuple						)		->       List[str]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =sorted(["""pos""", """neg"""]						)




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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =ImageClassification(image_column="""input_image"""    ,label_column="""input_label"""						)
         self.assertDictEqual({"""input_image""": """image""", """input_label""": """labels"""}    ,task.column_mapping						)




     def   __A    (     self      :			int						)		->       List[str]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =Features({"""image""": Image()}						)
         SCREAMING_SNAKE_CASE__       =Features({"""labels""": ClassLabel}						)
         SCREAMING_SNAKE_CASE__       ={
             """image_column""": """input_image""",
             """label_column""": """input_label""",
         }
         SCREAMING_SNAKE_CASE__       =ImageClassification.from_dict(_UpperCamelCase						)
         self.assertEqual("""image-classification"""    ,task.task						)
         self.assertEqual(_UpperCamelCase    ,task.input_schema						)
         self.assertEqual(_UpperCamelCase    ,task.label_schema						)




     def   __A    (     self      :			Tuple						)		->       Optional[int]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =ImageClassification(image_column="""input_image"""    ,label_column="""input_label"""						)
         self.assertEqual(task.label_schema["""labels"""]    ,_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =task.align_with_features(Features({"""input_label""": ClassLabel(names=self.labels						)}						)						)
         self.assertEqual(task.label_schema["""labels"""]    ,ClassLabel(names=self.labels						)						)






class 				__a (			__lowerCamelCase					):


     """simple docstring"""


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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =DatasetInfo(task_templates=QuestionAnsweringExtractive()						)
         SCREAMING_SNAKE_CASE__       =Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}    ,info=_UpperCamelCase						)
         assert isinstance(dataset.info.task_templates    ,_UpperCamelCase						)
         assert len(dataset.info.task_templates						) == 1

         def keep_task(_UpperCamelCase      :			Optional[int]						):
             return x

         def dont_keep_task(_UpperCamelCase      :			Optional[int]						):
             SCREAMING_SNAKE_CASE__       =deepcopy(_UpperCamelCase						)
             SCREAMING_SNAKE_CASE__       =0
             return out

         SCREAMING_SNAKE_CASE__       =dataset.map(_UpperCamelCase						)
         assert mapped_dataset.info.task_templates == dataset.info.task_templates
         # reload from cache
         SCREAMING_SNAKE_CASE__       =dataset.map(_UpperCamelCase						)
         assert mapped_dataset.info.task_templates == dataset.info.task_templates

         SCREAMING_SNAKE_CASE__       =dataset.map(_UpperCamelCase						)
         assert mapped_dataset.info.task_templates == []
         # reload from cache
         SCREAMING_SNAKE_CASE__       =dataset.map(_UpperCamelCase						)
         assert mapped_dataset.info.task_templates == []




     def   __A    (     self      :			List[str]						)		->       Any:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =Features({"""text""": Value("""string"""						), """label""": ClassLabel(names=("""pos""", """neg""")						)}						)
         SCREAMING_SNAKE_CASE__       =TextClassification(text_column="""text"""    ,label_column="""label"""						)
         SCREAMING_SNAKE_CASE__       =DatasetInfo(features=_UpperCamelCase    ,task_templates=_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =Dataset.from_dict({"""text""": ["""A sentence."""], """label""": ["""pos"""]}    ,info=_UpperCamelCase						)

         def process(_UpperCamelCase      :			List[str]						):
             return example

         SCREAMING_SNAKE_CASE__       =dataset.remove_columns("""label"""						)
         SCREAMING_SNAKE_CASE__       =modified_dataset.map(_UpperCamelCase						)
         assert mapped_dataset.info.task_templates == []







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 				__a (			__lowerCamelCase					):


     """simple docstring"""


     def   __A    (     self      :			Optional[Any]    ,_UpperCamelCase      :			Optional[int]    ,_UpperCamelCase      :			Optional[Any]=False						)		->       Tuple:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__      ,		SCREAMING_SNAKE_CASE__      ,		SCREAMING_SNAKE_CASE__       =(
             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()
         SCREAMING_SNAKE_CASE__       =pa.Table.from_pydict({"""filename""": [Path(_UpperCamelCase						).name] * 1_0_0}						)
         if take == -1:
             SCREAMING_SNAKE_CASE__       =len(_UpperCamelCase						) - skip
         if skip is not None and take is not None:
             SCREAMING_SNAKE_CASE__       =pa_table.slice(_UpperCamelCase    ,_UpperCamelCase						)
         return pa_table






class 				__a (			__lowerCamelCase					):


     """simple docstring"""


     def   __A    (     self      :			List[str]						)		->       Any:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       ="""my_name"""
         SCREAMING_SNAKE_CASE__       =SplitInfo(name="""train"""    ,num_examples=1_0_0						)
         SCREAMING_SNAKE_CASE__       =SplitInfo(name="""test"""    ,num_examples=1_0_0						)
         SCREAMING_SNAKE_CASE__       =[train_info, test_info]
         SCREAMING_SNAKE_CASE__       =SplitDict()
         split_dict.add(_UpperCamelCase						)
         split_dict.add(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =DatasetInfo(splits=_UpperCamelCase						)

         with tempfile.TemporaryDirectory() as tmp_dir:
             SCREAMING_SNAKE_CASE__       =ReaderTest(_UpperCamelCase    ,_UpperCamelCase						)

             SCREAMING_SNAKE_CASE__       ="""test[:33%]"""
             SCREAMING_SNAKE_CASE__       =Dataset(**reader.read(_UpperCamelCase    ,_UpperCamelCase    ,_UpperCamelCase						)						)
             self.assertEqual(dset["""filename"""][0]    ,f"""{name}-test"""						)
             self.assertEqual(dset.num_rows    ,3_3						)
             self.assertEqual(dset.num_columns    ,1						)

             SCREAMING_SNAKE_CASE__       =["""train""", """test[:33%]"""]
             SCREAMING_SNAKE_CASE__       =[Split.TRAIN, ReadInstruction.from_spec("""test[:33%]"""						)]
             for instructions in [instructionsa, instructionsa]:
                 SCREAMING_SNAKE_CASE__       =[reader.read(_UpperCamelCase    ,_UpperCamelCase    ,_UpperCamelCase						) for instr in instructions]
                 SCREAMING_SNAKE_CASE__      ,		SCREAMING_SNAKE_CASE__       =(Dataset(**_UpperCamelCase						) for dataset_kwargs in datasets_kwargs)
                 self.assertEqual(train_dset["""filename"""][0]    ,f"""{name}-train"""						)
                 self.assertEqual(train_dset.num_rows    ,1_0_0						)
                 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    ,3_3						)
                 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   __A    (     self      :			Tuple						)		->       str:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       ="""my_name"""
         SCREAMING_SNAKE_CASE__       =SplitInfo(name="""train"""    ,num_examples=1_0_0_0    ,shard_lengths=[1_0_0] * 1_0						)
         SCREAMING_SNAKE_CASE__       =[train_info]
         SCREAMING_SNAKE_CASE__       =SplitDict()
         split_dict.add(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =DatasetInfo(splits=_UpperCamelCase						)

         with tempfile.TemporaryDirectory() as tmp_dir:
             SCREAMING_SNAKE_CASE__       =ReaderTest(_UpperCamelCase    ,_UpperCamelCase						)

             SCREAMING_SNAKE_CASE__       ="""train[:33%]"""
             SCREAMING_SNAKE_CASE__       =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    ,3_3_0						)
             self.assertEqual(dset.num_columns    ,1						)




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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =SplitInfo(name="""train"""    ,num_examples=1_0_0						)
         SCREAMING_SNAKE_CASE__       =SplitInfo(name="""test"""    ,num_examples=1_0_0						)
         SCREAMING_SNAKE_CASE__       =SplitDict()
         split_dict.add(_UpperCamelCase						)
         split_dict.add(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =DatasetInfo(splits=_UpperCamelCase						)

         with tempfile.TemporaryDirectory() as tmp_dir:
             SCREAMING_SNAKE_CASE__       =ReaderTest(_UpperCamelCase    ,_UpperCamelCase						)

             SCREAMING_SNAKE_CASE__       =[
                 {"""filename""": os.path.join(_UpperCamelCase    ,"""train"""						)},
                 {"""filename""": os.path.join(_UpperCamelCase    ,"""test"""						), """skip""": 1_0, """take""": 1_0},
             ]
             SCREAMING_SNAKE_CASE__       =Dataset(**reader.read_files(_UpperCamelCase    ,original_instructions="""train+test[10:20]"""						)						)
             self.assertEqual(dset.num_rows    ,1_1_0						)
             self.assertEqual(dset.num_columns    ,1						)
             del dset






@pytest.mark.parametrize("""in_memory""",    [False, True]     )
def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =arrow_file
    with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
        SCREAMING_SNAKE_CASE__       =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,    __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =arrow_file
    SCREAMING_SNAKE_CASE__       =ArrowReader("""""",    __UpperCamelCase     )
    with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
        SCREAMING_SNAKE_CASE__       =reader.read_files([{"""filename""": filename}],    in_memory=__UpperCamelCase     )
    assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
    SCREAMING_SNAKE_CASE__       =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_   (     ):
    assert ReadInstruction("""train""",    to=10,    unit="""abs"""     ).to_spec() == "train[:10]"
    assert ReadInstruction("""train""",    from_=-80,    to=10,    unit="""%"""     ).to_spec() == "train[-80%:10%]"

    SCREAMING_SNAKE_CASE__       ="""train+test"""
    assert ReadInstruction.from_spec(__UpperCamelCase     ).to_spec() == spec_train_test

    SCREAMING_SNAKE_CASE__       ="""train[2:10]"""
    assert ReadInstruction.from_spec(__UpperCamelCase     ).to_spec() == spec_train_abs

    SCREAMING_SNAKE_CASE__       ="""train[15%:-20%]"""
    assert ReadInstruction.from_spec(__UpperCamelCase     ).to_spec() == spec_train_pct

    SCREAMING_SNAKE_CASE__       ="""train[:10%](closest)"""
    assert ReadInstruction.from_spec(__UpperCamelCase     ).to_spec() == "train[:10%]"

    SCREAMING_SNAKE_CASE__       ="""train[:10%](pct1_dropremainder)"""
    assert ReadInstruction.from_spec(__UpperCamelCase     ).to_spec() == spec_train_pct_rounding

    SCREAMING_SNAKE_CASE__       ="""train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"""
    assert ReadInstruction.from_spec(__UpperCamelCase     ).to_spec() == spec_train_test_pct_rounding



def      UpperCAmelCase_   (     ):
    SCREAMING_SNAKE_CASE__       ="""dummy"""
    SCREAMING_SNAKE_CASE__       =[SplitInfo(name="""train""",    num_examples=100     )]
    SCREAMING_SNAKE_CASE__       ="""train[:33%]"""
    SCREAMING_SNAKE_CASE__       ="""arrow"""
    SCREAMING_SNAKE_CASE__       ="""prefix"""

    SCREAMING_SNAKE_CASE__       =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}
    ]

    SCREAMING_SNAKE_CASE__       =[SplitInfo(name="""train""",    num_examples=100,    shard_lengths=[10] * 10     )]
    SCREAMING_SNAKE_CASE__       =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,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =[SplitInfo(name="""train""",    num_examples=100     )]
    SCREAMING_SNAKE_CASE__       ="""train"""
    SCREAMING_SNAKE_CASE__       ="""arrow"""
    SCREAMING_SNAKE_CASE__       ="""prefix_path"""
    with pytest.raises(__UpperCamelCase     ):
        SCREAMING_SNAKE_CASE__       =make_file_instructions(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     )






