





import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
       from PIL import Image

       from transformers import FlavaImageProcessor, FlavaProcessor
       from transformers.models.flava.image_processing_flava import (
           FLAVA_CODEBOOK_MEAN,
           FLAVA_CODEBOOK_STD,
           FLAVA_IMAGE_MEAN,
           FLAVA_IMAGE_STD,
       )





@require_vision
class 	lowerCAmelCase__(       unittest.TestCase			):






       '''simple docstring'''


       def 			UpperCamelCase_	(					self							)	->   str:
            _SCREAMING_SNAKE_CASE				:		str  			=							tempfile.mkdtemp()

            # fmt: off
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
            # fmt: on
            _SCREAMING_SNAKE_CASE				:		int  			=							os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES["vocab_file"]							)

            with open(self.vocab_file			,  "w"			,  encoding="utf-8"							) as fp:
                 fp.write("".join([x + "\n" for x in vocab_tokens]							)							)

            _SCREAMING_SNAKE_CASE				:		Any  			=							{
                "image_mean": FLAVA_IMAGE_MEAN,
                "image_std": FLAVA_IMAGE_STD,
                "do_normalize": True,
                "do_resize": True,
                "size": 2_2_4,
                "do_center_crop": True,
                "crop_size": 2_2_4,
                "input_size_patches": 1_4,
                "total_mask_patches": 7_5,
                "mask_group_max_patches": None,
                "mask_group_min_patches": 1_6,
                "mask_group_min_aspect_ratio": 0.3,
                "mask_group_max_aspect_ratio": None,
                "codebook_do_resize": True,
                "codebook_size": 1_1_2,
                "codebook_do_center_crop": True,
                "codebook_crop_size": 1_1_2,
                "codebook_do_map_pixels": True,
                "codebook_do_normalize": True,
                "codebook_image_mean": FLAVA_CODEBOOK_MEAN,
                "codebook_image_std": FLAVA_CODEBOOK_STD,
            }

            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							os.path.join(self.tmpdirname			,  __lowerCamelCase							)
            with open(self.image_processor_file			,  "w"			,  encoding="utf-8"							) as fp:
                 json.dump(__lowerCamelCase			,  __lowerCamelCase							)




       def 			UpperCamelCase_	(					self			,  **__lowerCamelCase							)	->   List[str]:
            return BertTokenizer.from_pretrained(self.tmpdirname			,  **__lowerCamelCase							)




       def 			UpperCamelCase_	(					self			,  **__lowerCamelCase							)	->   Optional[Any]:
            return BertTokenizerFast.from_pretrained(self.tmpdirname			,  **__lowerCamelCase							)




       def 			UpperCamelCase_	(					self			,  **__lowerCamelCase							)	->   Tuple:
            return FlavaImageProcessor.from_pretrained(self.tmpdirname			,  **__lowerCamelCase							)




       def 			UpperCamelCase_	(					self							)	->   int:
            shutil.rmtree(self.tmpdirname							)




       def 			UpperCamelCase_	(					self							)	->   Optional[int]:
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							[np.random.randint(2_5_5			,  size=(3, 3_0, 4_0_0)			,  dtype=np.uinta							)]

            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							[Image.fromarray(np.moveaxis(__lowerCamelCase			,  0			,  -1							)							) for x in image_inputs]

            return image_inputs




       def 			UpperCamelCase_	(					self							)	->   Any:
            _SCREAMING_SNAKE_CASE				:		Dict  			=							self.get_tokenizer()
            _SCREAMING_SNAKE_CASE				:		Any  			=							self.get_rust_tokenizer()
            _SCREAMING_SNAKE_CASE				:		Dict  			=							self.get_image_processor()

            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							FlavaProcessor(tokenizer=__lowerCamelCase			,  image_processor=__lowerCamelCase							)
            processor_slow.save_pretrained(self.tmpdirname							)
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							FlavaProcessor.from_pretrained(self.tmpdirname			,  use_fast=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							FlavaProcessor(tokenizer=__lowerCamelCase			,  image_processor=__lowerCamelCase							)
            processor_fast.save_pretrained(self.tmpdirname							)
            _SCREAMING_SNAKE_CASE				:		int  			=							FlavaProcessor.from_pretrained(self.tmpdirname							)

            self.assertEqual(processor_slow.tokenizer.get_vocab()			,  tokenizer_slow.get_vocab()							)
            self.assertEqual(processor_fast.tokenizer.get_vocab()			,  tokenizer_fast.get_vocab()							)
            self.assertEqual(tokenizer_slow.get_vocab()			,  tokenizer_fast.get_vocab()							)
            self.assertIsInstance(processor_slow.tokenizer			,  __lowerCamelCase							)
            self.assertIsInstance(processor_fast.tokenizer			,  __lowerCamelCase							)

            self.assertEqual(processor_slow.image_processor.to_json_string()			,  image_processor.to_json_string()							)
            self.assertEqual(processor_fast.image_processor.to_json_string()			,  image_processor.to_json_string()							)
            self.assertIsInstance(processor_slow.image_processor			,  __lowerCamelCase							)
            self.assertIsInstance(processor_fast.image_processor			,  __lowerCamelCase							)




       def 			UpperCamelCase_	(					self							)	->   Optional[int]:
            _SCREAMING_SNAKE_CASE				:		Dict  			=							FlavaProcessor(tokenizer=self.get_tokenizer()			,  image_processor=self.get_image_processor()							)
            processor.save_pretrained(self.tmpdirname							)

            _SCREAMING_SNAKE_CASE				:		str  			=							self.get_tokenizer(bos_token="(BOS)"			,  eos_token="(EOS)"							)
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							self.get_image_processor(do_normalize=__lowerCamelCase			,  padding_value=1.0							)

            _SCREAMING_SNAKE_CASE				:		Dict  			=							FlavaProcessor.from_pretrained(
                self.tmpdirname			,  bos_token="(BOS)"			,  eos_token="(EOS)"			,  do_normalize=__lowerCamelCase			,  padding_value=1.0							)

            self.assertEqual(processor.tokenizer.get_vocab()			,  tokenizer_add_kwargs.get_vocab()							)
            self.assertIsInstance(processor.tokenizer			,  __lowerCamelCase							)

            self.assertEqual(processor.image_processor.to_json_string()			,  image_processor_add_kwargs.to_json_string()							)
            self.assertIsInstance(processor.image_processor			,  __lowerCamelCase							)




       def 			UpperCamelCase_	(					self							)	->   Any:
            _SCREAMING_SNAKE_CASE				:		str  			=							self.get_image_processor()
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE				:		Any  			=							FlavaProcessor(tokenizer=__lowerCamelCase			,  image_processor=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		int  			=							self.prepare_image_inputs()

            _SCREAMING_SNAKE_CASE				:		List[str]  			=							image_processor(__lowerCamelCase			,  return_tensors="np"							)
            _SCREAMING_SNAKE_CASE				:		str  			=							processor(images=__lowerCamelCase			,  return_tensors="np"							)

            for key in input_feat_extract.keys():
                 self.assertAlmostEqual(input_feat_extract[key].sum()			,  input_processor[key].sum()			,  delta=1E-2							)

            # With rest of the args
            random.seed(1_2_3_4							)
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							image_processor(
                __lowerCamelCase			,  return_image_mask=__lowerCamelCase			,  return_codebook_pixels=__lowerCamelCase			,  return_tensors="np"							)
            random.seed(1_2_3_4							)
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							processor(
                images=__lowerCamelCase			,  return_image_mask=__lowerCamelCase			,  return_codebook_pixels=__lowerCamelCase			,  return_tensors="np"							)

            for key in input_feat_extract.keys():
                 self.assertAlmostEqual(input_feat_extract[key].sum()			,  input_processor[key].sum()			,  delta=1E-2							)




       def 			UpperCamelCase_	(					self							)	->   int:
            _SCREAMING_SNAKE_CASE				:		Any  			=							self.get_image_processor()
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE				:		int  			=							FlavaProcessor(tokenizer=__lowerCamelCase			,  image_processor=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							"lower newer"

            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							processor(text=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		List[str]  			=							tokenizer(__lowerCamelCase							)

            for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]			,  encoded_processor[key]							)




       def 			UpperCamelCase_	(					self							)	->   Tuple:
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							self.get_image_processor()
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							FlavaProcessor(tokenizer=__lowerCamelCase			,  image_processor=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		Any  			=							"lower newer"
            _SCREAMING_SNAKE_CASE				:		Dict  			=							self.prepare_image_inputs()

            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							processor(text=__lowerCamelCase			,  images=__lowerCamelCase							)

            self.assertListEqual(list(inputs.keys()							)			,  ["input_ids", "token_type_ids", "attention_mask", "pixel_values"]							)

            # add extra args
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							processor(text=__lowerCamelCase			,  images=__lowerCamelCase			,  return_codebook_pixels=__lowerCamelCase			,  return_image_mask=__lowerCamelCase							)

            self.assertListEqual(
                list(inputs.keys()							)			,  [
                    "input_ids",
                    "token_type_ids",
                    "attention_mask",
                    "pixel_values",
                    "codebook_pixel_values",
                    "bool_masked_pos",
                ]			,  )

            # test if it raises when no input is passed
            with pytest.raises(__lowerCamelCase							):
                 processor()




       def 			UpperCamelCase_	(					self							)	->   Dict:
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							self.get_image_processor()
            _SCREAMING_SNAKE_CASE				:		str  			=							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE				:		int  			=							FlavaProcessor(tokenizer=__lowerCamelCase			,  image_processor=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            _SCREAMING_SNAKE_CASE				:		Dict  			=							processor.batch_decode(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		Any  			=							tokenizer.batch_decode(__lowerCamelCase							)

            self.assertListEqual(__lowerCamelCase			,  __lowerCamelCase							)







       def 			UpperCamelCase_	(					self							)	->   Any:
            _SCREAMING_SNAKE_CASE				:		int  			=							self.get_image_processor()
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE				:		Dict  			=							FlavaProcessor(tokenizer=__lowerCamelCase			,  image_processor=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							"lower newer"
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							self.prepare_image_inputs()

            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							processor(text=__lowerCamelCase			,  images=__lowerCamelCase							)

            self.assertListEqual(list(inputs.keys()							)			,  processor.model_input_names							)






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 	lowerCAmelCase__(       __lowercase			):






       '''simple docstring'''


       def 			UpperCamelCase_	(					self			,  __lowerCamelCase			,  __lowerCamelCase=False							)	->   List[Any]:
            _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							(
                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(__lowerCamelCase							)			,  "wb"							).close()
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							pa.Table.from_pydict({"filename": [Path(__lowerCamelCase							).name] * 1_0_0}							)
            if take == -1:
                 _SCREAMING_SNAKE_CASE				:		int  			=							len(__lowerCamelCase							) - skip
            if skip is not None and take is not None:
                 _SCREAMING_SNAKE_CASE				:		List[str]  			=							pa_table.slice(__lowerCamelCase			,  __lowerCamelCase							)
            return pa_table





class 	lowerCAmelCase__(       __lowercase			):






       '''simple docstring'''


       def 			UpperCamelCase_	(					self							)	->   List[Any]:
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							"my_name"
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							SplitInfo(name="train"			,  num_examples=1_0_0							)
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							SplitInfo(name="test"			,  num_examples=1_0_0							)
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							[train_info, test_info]
            _SCREAMING_SNAKE_CASE				:		str  			=							SplitDict()
            split_dict.add(__lowerCamelCase							)
            split_dict.add(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		str  			=							DatasetInfo(splits=__lowerCamelCase							)

            with tempfile.TemporaryDirectory() as tmp_dir:
                 _SCREAMING_SNAKE_CASE				:		Tuple  			=							ReaderTest(__lowerCamelCase			,  __lowerCamelCase							)

                 _SCREAMING_SNAKE_CASE				:		Dict  			=							"test[:33%]"
                 _SCREAMING_SNAKE_CASE				:		Tuple  			=							Dataset(**reader.read(__lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)							)
                 self.assertEqual(dset["filename"][0]			,  F"""{name}-test"""							)
                 self.assertEqual(dset.num_rows			,  3_3							)
                 self.assertEqual(dset.num_columns			,  1							)

                 _SCREAMING_SNAKE_CASE				:		List[str]  			=							["train", "test[:33%]"]
                 _SCREAMING_SNAKE_CASE				:		Tuple  			=							[Split.TRAIN, ReadInstruction.from_spec("test[:33%]"							)]
                 for instructions in [instructionsa, instructionsa]:
                      _SCREAMING_SNAKE_CASE				:		List[Any]  			=							[reader.read(__lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							) for instr in instructions]
                      _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		Any  			=							(Dataset(**__lowerCamelCase							) 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			,  __lowerCamelCase							)
                      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			,  __lowerCamelCase							)
                      self.assertEqual(str(test_dset.split							)			,  "test[:33%]"							)
                      del train_dset, test_dset




       def 			UpperCamelCase_	(					self							)	->   int:
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							"my_name"
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							SplitInfo(name="train"			,  num_examples=1_0_0_0			,  shard_lengths=[1_0_0] * 1_0							)
            _SCREAMING_SNAKE_CASE				:		str  			=							[train_info]
            _SCREAMING_SNAKE_CASE				:		Dict  			=							SplitDict()
            split_dict.add(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							DatasetInfo(splits=__lowerCamelCase							)

            with tempfile.TemporaryDirectory() as tmp_dir:
                 _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							ReaderTest(__lowerCamelCase			,  __lowerCamelCase							)

                 _SCREAMING_SNAKE_CASE				:		int  			=							"train[:33%]"
                 _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							Dataset(**reader.read(__lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)							)
                 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 			UpperCamelCase_	(					self							)	->   str:
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							SplitInfo(name="train"			,  num_examples=1_0_0							)
            _SCREAMING_SNAKE_CASE				:		Dict  			=							SplitInfo(name="test"			,  num_examples=1_0_0							)
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							SplitDict()
            split_dict.add(__lowerCamelCase							)
            split_dict.add(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		Any  			=							DatasetInfo(splits=__lowerCamelCase							)

            with tempfile.TemporaryDirectory() as tmp_dir:
                 _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							ReaderTest(__lowerCamelCase			,  __lowerCamelCase							)

                 _SCREAMING_SNAKE_CASE				:		List[Any]  			=							[
                     {"filename": os.path.join(__lowerCamelCase			,  "train"							)},
                     {"filename": os.path.join(__lowerCamelCase			,  "test"							), "skip": 1_0, "take": 1_0},
                 ]
                 _SCREAMING_SNAKE_CASE				:		Any  			=							Dataset(**reader.read_files(__lowerCamelCase			,  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 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase		):
     _SCREAMING_SNAKE_CASE				:		Dict  			=							arrow_file
     with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
          _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							ArrowReader.read_table(__lowerCamelCase,				in_memory=__lowerCamelCase		)
     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 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase		):
     _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							arrow_file
     _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							ArrowReader("",				__lowerCamelCase		)
     with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
          _SCREAMING_SNAKE_CASE				:		Dict  			=							reader.read_files([{"filename": filename}],				in_memory=__lowerCamelCase		)
     assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
     _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							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 			lowerCamelCase__	():
     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				:		str  			=							"train+test"
     assert ReadInstruction.from_spec(__lowerCamelCase		).to_spec() == spec_train_test

     _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							"train[2:10]"
     assert ReadInstruction.from_spec(__lowerCamelCase		).to_spec() == spec_train_abs

     _SCREAMING_SNAKE_CASE				:		List[Any]  			=							"train[15%:-20%]"
     assert ReadInstruction.from_spec(__lowerCamelCase		).to_spec() == spec_train_pct

     _SCREAMING_SNAKE_CASE				:		List[str]  			=							"train[:10%](closest)"
     assert ReadInstruction.from_spec(__lowerCamelCase		).to_spec() == "train[:10%]"

     _SCREAMING_SNAKE_CASE				:		Any  			=							"train[:10%](pct1_dropremainder)"
     assert ReadInstruction.from_spec(__lowerCamelCase		).to_spec() == spec_train_pct_rounding

     _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							"train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"
     assert ReadInstruction.from_spec(__lowerCamelCase		).to_spec() == spec_train_test_pct_rounding






def 			lowerCamelCase__	():
     _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							"dummy"
     _SCREAMING_SNAKE_CASE				:		List[str]  			=							[SplitInfo(name="train",				num_examples=100		)]
     _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							"train[:33%]"
     _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							"arrow"
     _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							"prefix"

     _SCREAMING_SNAKE_CASE				:		Any  			=							make_file_instructions(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase		)
     assert isinstance(__lowerCamelCase,				__lowerCamelCase		)
     assert file_instructions.num_examples == 33
     assert file_instructions.file_instructions == [
         {"filename": os.path.join(__lowerCamelCase,				f"""{name}-train.arrow"""		), "skip": 0, "take": 33}
     ]

     _SCREAMING_SNAKE_CASE				:		List[str]  			=							[SplitInfo(name="train",				num_examples=100,				shard_lengths=[10] * 10		)]
     _SCREAMING_SNAKE_CASE				:		Tuple  			=							make_file_instructions(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase		)
     assert isinstance(__lowerCamelCase,				__lowerCamelCase		)
     assert file_instructions.num_examples == 33
     assert file_instructions.file_instructions == [
         {"filename": os.path.join(__lowerCamelCase,				f"""{name}-train-00000-of-00010.arrow"""		), "skip": 0, "take": -1},
         {"filename": os.path.join(__lowerCamelCase,				f"""{name}-train-00001-of-00010.arrow"""		), "skip": 0, "take": -1},
         {"filename": os.path.join(__lowerCamelCase,				f"""{name}-train-00002-of-00010.arrow"""		), "skip": 0, "take": -1},
         {"filename": os.path.join(__lowerCamelCase,				f"""{name}-train-00003-of-00010.arrow"""		), "skip": 0, "take": 3},
     ]






@pytest.mark.parametrize("name, expected_exception",				[(None, TypeError), ("", ValueError)]		)
def 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase		):
     _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							[SplitInfo(name="train",				num_examples=100		)]
     _SCREAMING_SNAKE_CASE				:		int  			=							"train"
     _SCREAMING_SNAKE_CASE				:		int  			=							"arrow"
     _SCREAMING_SNAKE_CASE				:		Tuple  			=							"prefix_path"
     with pytest.raises(__lowerCamelCase		):
          _SCREAMING_SNAKE_CASE				:		List[str]  			=							make_file_instructions(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase		)