
'''simple docstring'''







from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


_snake_case					: int     			=					logging.get_logger(__name__)

_snake_case					: Dict     			=					{
    """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json""",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}


class       lowerCAmelCase			(					__UpperCAmelCase      ):
		a				:       Union[str, Any]							=       'blenderbot'
		a				:       List[str]							=       ['past_key_values']
		a				:       Any							=       {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




		def __init__(					self       ,    UpperCamelCase=8_008       ,    UpperCamelCase=128       ,    UpperCamelCase=2       ,    UpperCamelCase=10_240       ,    UpperCamelCase=32       ,    UpperCamelCase=24       ,    UpperCamelCase=10_240       ,    UpperCamelCase=32       ,    UpperCamelCase=0.0       ,    UpperCamelCase=0.0       ,    UpperCamelCase=True       ,    UpperCamelCase=True       ,    UpperCamelCase="gelu"       ,    UpperCamelCase=2_560       ,    UpperCamelCase=0.1       ,    UpperCamelCase=0.0       ,    UpperCamelCase=0.0       ,    UpperCamelCase=0.02       ,    UpperCamelCase=1       ,    UpperCamelCase=False       ,    UpperCamelCase=0       ,    UpperCamelCase=1       ,    UpperCamelCase=2       ,    UpperCamelCase=3       ,    UpperCamelCase=2       ,    **UpperCamelCase       ,    ):
								_SCREAMING_SNAKE_CASE					=			vocab_size
								_SCREAMING_SNAKE_CASE					=			max_position_embeddings
								_SCREAMING_SNAKE_CASE					=			d_model
								_SCREAMING_SNAKE_CASE					=			encoder_ffn_dim
								_SCREAMING_SNAKE_CASE					=			encoder_layers
								_SCREAMING_SNAKE_CASE					=			encoder_attention_heads
								_SCREAMING_SNAKE_CASE					=			decoder_ffn_dim
								_SCREAMING_SNAKE_CASE					=			decoder_layers
								_SCREAMING_SNAKE_CASE					=			decoder_attention_heads
								_SCREAMING_SNAKE_CASE					=			dropout
								_SCREAMING_SNAKE_CASE					=			attention_dropout
								_SCREAMING_SNAKE_CASE					=			activation_dropout
								_SCREAMING_SNAKE_CASE					=			activation_function
								_SCREAMING_SNAKE_CASE					=			init_std
								_SCREAMING_SNAKE_CASE					=			encoder_layerdrop
								_SCREAMING_SNAKE_CASE					=			decoder_layerdrop
								_SCREAMING_SNAKE_CASE					=			use_cache
								_SCREAMING_SNAKE_CASE					=			encoder_layers
								_SCREAMING_SNAKE_CASE					=			scale_embedding  # scale factor will be sqrt(d_model) if True

								super().__init__(
								    pad_token_id=_UpperCAmelCase       ,    bos_token_id=_UpperCAmelCase       ,    eos_token_id=_UpperCAmelCase       ,    is_encoder_decoder=_UpperCAmelCase       ,    decoder_start_token_id=_UpperCAmelCase       ,    encoder_no_repeat_ngram_size=_UpperCAmelCase       ,    forced_eos_token_id=_UpperCAmelCase       ,    **_UpperCAmelCase       ,    )


class       lowerCAmelCase			(					__UpperCAmelCase      ):




		@property
		def      lowercase							(					self   ):
								if self.task in ["default", "seq2seq-lm"]:
														_SCREAMING_SNAKE_CASE					=			OrderedDict(
														    [
														        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
														        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
														    ]   )
														if self.use_past:
																				_SCREAMING_SNAKE_CASE					=			{0: "batch"}
																				_SCREAMING_SNAKE_CASE					=			{0: "batch", 1: "past_decoder_sequence + sequence"}
														else:
																				_SCREAMING_SNAKE_CASE					=			{0: "batch", 1: "decoder_sequence"}
																				_SCREAMING_SNAKE_CASE					=			{0: "batch", 1: "decoder_sequence"}
														if self.use_past:
																				self.fill_with_past_key_values_(_UpperCAmelCase       ,    direction="inputs"   )
								elif self.task == "causal-lm":
														_SCREAMING_SNAKE_CASE					=			OrderedDict(
														    [
														        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
														        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
														    ]   )
														if self.use_past:
																				_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			self.num_layers
																				for i in range(_UpperCAmelCase   ):
																										_SCREAMING_SNAKE_CASE					=			{0: "batch", 2: "past_sequence + sequence"}
																										_SCREAMING_SNAKE_CASE					=			{0: "batch", 2: "past_sequence + sequence"}
								else:
														_SCREAMING_SNAKE_CASE					=			OrderedDict(
														    [
														        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
														        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
														        ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
														        ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
														    ]   )

								return common_inputs




		@property
		# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
		def      lowercase							(					self   ):
								if self.task in ["default", "seq2seq-lm"]:
														_SCREAMING_SNAKE_CASE					=			super().outputs
								else:
														_SCREAMING_SNAKE_CASE					=			super(_UpperCAmelCase       ,    self   ).outputs
														if self.use_past:
																				_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			self.num_layers
																				for i in range(_UpperCAmelCase   ):
																										_SCREAMING_SNAKE_CASE					=			{0: "batch", 2: "past_sequence + sequence"}
																										_SCREAMING_SNAKE_CASE					=			{0: "batch", 2: "past_sequence + sequence"}
								return common_outputs




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase = -1       ,    UpperCamelCase = -1       ,    UpperCamelCase = False       ,    UpperCamelCase = None       ,    ):
								_SCREAMING_SNAKE_CASE					=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    _UpperCAmelCase       ,    _UpperCAmelCase       ,    _UpperCAmelCase       ,    _UpperCAmelCase       ,    _UpperCAmelCase   )
								# Generate decoder inputs
								_SCREAMING_SNAKE_CASE					=			seq_length if not self.use_past else 1
								_SCREAMING_SNAKE_CASE					=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    _UpperCAmelCase       ,    _UpperCAmelCase       ,    _UpperCAmelCase       ,    _UpperCAmelCase       ,    _UpperCAmelCase   )
								_SCREAMING_SNAKE_CASE					=			{F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
								_SCREAMING_SNAKE_CASE					=			dict(**_UpperCAmelCase       ,    **_UpperCAmelCase   )

								if self.use_past:
														if not is_torch_available():
																				raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."   )
														else:
																				import torch
														_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			common_inputs["input_ids"].shape
														_SCREAMING_SNAKE_CASE					=			common_inputs["decoder_input_ids"].shape[1]
														_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			self.num_attention_heads
														_SCREAMING_SNAKE_CASE					=			(
														    batch,
														    num_encoder_attention_heads,
														    encoder_seq_length,
														    self._config.hidden_size // num_encoder_attention_heads,
														)
														_SCREAMING_SNAKE_CASE					=			decoder_seq_length
														_SCREAMING_SNAKE_CASE					=			(
														    batch,
														    num_decoder_attention_heads,
														    decoder_past_length,
														    self._config.hidden_size // num_decoder_attention_heads,
														)
														_SCREAMING_SNAKE_CASE					=			torch.cat(
														    [common_inputs["decoder_attention_mask"], torch.ones(_UpperCAmelCase       ,    _UpperCAmelCase   )]       ,    dim=1   )
														_SCREAMING_SNAKE_CASE					=			[]
														_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			self.num_layers

														for _ in range(_UpperCAmelCase   ):
																				common_inputs["past_key_values"].append(
																				    (
																				        torch.zeros(_UpperCAmelCase   ),
																				        torch.zeros(_UpperCAmelCase   ),
																				        torch.zeros(_UpperCAmelCase   ),
																				        torch.zeros(_UpperCAmelCase   ),
																				    )   )
								return common_inputs




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase = -1       ,    UpperCamelCase = -1       ,    UpperCamelCase = False       ,    UpperCamelCase = None       ,    ):
								_SCREAMING_SNAKE_CASE					=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    _UpperCAmelCase       ,    _UpperCAmelCase       ,    _UpperCAmelCase       ,    _UpperCAmelCase       ,    _UpperCAmelCase   )

								if self.use_past:
														if not is_torch_available():
																				raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."   )
														else:
																				import torch
														_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			common_inputs["input_ids"].shape
														_SCREAMING_SNAKE_CASE					=			seqlen
														_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			self.num_layers
														_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			self.num_attention_heads
														_SCREAMING_SNAKE_CASE					=			(
														    batch,
														    num_encoder_attention_heads,
														    past_key_values_length,
														    self._config.hidden_size // num_encoder_attention_heads,
														)
														_SCREAMING_SNAKE_CASE					=			common_inputs["attention_mask"].dtype
														_SCREAMING_SNAKE_CASE					=			torch.cat(
														    [common_inputs["attention_mask"], torch.ones(_UpperCAmelCase       ,    _UpperCAmelCase       ,    dtype=_UpperCAmelCase   )]       ,    dim=1   )
														_SCREAMING_SNAKE_CASE					=			[
														    (torch.zeros(_UpperCAmelCase   ), torch.zeros(_UpperCAmelCase   )) for _ in range(_UpperCAmelCase   )
														]
								return common_inputs




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase = -1       ,    UpperCamelCase = -1       ,    UpperCamelCase = False       ,    UpperCamelCase = None       ,    ):
								# Copied from OnnxConfig.generate_dummy_inputs
								# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
								# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
								_SCREAMING_SNAKE_CASE					=			compute_effective_axis_dimension(
								    _UpperCAmelCase       ,    fixed_dimension=OnnxConfig.default_fixed_batch       ,    num_token_to_add=0   )

								# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
								_SCREAMING_SNAKE_CASE					=			tokenizer.num_special_tokens_to_add(_UpperCAmelCase   )
								_SCREAMING_SNAKE_CASE					=			compute_effective_axis_dimension(
								    _UpperCAmelCase       ,    fixed_dimension=OnnxConfig.default_fixed_sequence       ,    num_token_to_add=_UpperCAmelCase   )

								# Generate dummy inputs according to compute batch and sequence
								_SCREAMING_SNAKE_CASE					=			[" ".join([tokenizer.unk_token]   ) * seq_length] * batch_size
								_SCREAMING_SNAKE_CASE					=			dict(tokenizer(_UpperCAmelCase       ,    return_tensors=_UpperCAmelCase   )   )
								return common_inputs




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase = -1       ,    UpperCamelCase = -1       ,    UpperCamelCase = False       ,    UpperCamelCase = None       ,    ):
								if self.task in ["default", "seq2seq-lm"]:
														_SCREAMING_SNAKE_CASE					=			self._generate_dummy_inputs_for_default_and_seqaseq_lm(
														    _UpperCAmelCase       ,    batch_size=_UpperCAmelCase       ,    seq_length=_UpperCAmelCase       ,    is_pair=_UpperCAmelCase       ,    framework=_UpperCAmelCase   )

								elif self.task == "causal-lm":
														_SCREAMING_SNAKE_CASE					=			self._generate_dummy_inputs_for_causal_lm(
														    _UpperCAmelCase       ,    batch_size=_UpperCAmelCase       ,    seq_length=_UpperCAmelCase       ,    is_pair=_UpperCAmelCase       ,    framework=_UpperCAmelCase   )
								else:
														_SCREAMING_SNAKE_CASE					=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
														    _UpperCAmelCase       ,    batch_size=_UpperCAmelCase       ,    seq_length=_UpperCAmelCase       ,    is_pair=_UpperCAmelCase       ,    framework=_UpperCAmelCase   )

								return common_inputs




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   ):
								if self.task in ["default", "seq2seq-lm"]:
														_SCREAMING_SNAKE_CASE					=			super()._flatten_past_key_values_(_UpperCAmelCase       ,    _UpperCAmelCase       ,    _UpperCAmelCase       ,    _UpperCAmelCase   )
								else:
														_SCREAMING_SNAKE_CASE					=			super(_UpperCAmelCase       ,    self   )._flatten_past_key_values_(
														    _UpperCAmelCase       ,    _UpperCAmelCase       ,    _UpperCAmelCase       ,    _UpperCAmelCase   )




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase   ):
								if direction not in ["inputs", "outputs"]:
														raise ValueError(F'direction must either be "inputs" or "outputs", but {direction} was given'   )

								_SCREAMING_SNAKE_CASE					=			"past_key_values" if direction == "inputs" else "present"
								_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			self.num_layers

								_SCREAMING_SNAKE_CASE					=			"past_encoder_sequence"
								_SCREAMING_SNAKE_CASE					=			"past_decoder_sequence" if direction == "inputs" else "past_decoder_sequence + sequence"

								for i in range(_UpperCAmelCase   ):
														_SCREAMING_SNAKE_CASE					=			{0: "batch", 2: decoder_sequence}
														_SCREAMING_SNAKE_CASE					=			{0: "batch", 2: decoder_sequence}
														_SCREAMING_SNAKE_CASE					=			{0: "batch", 2: encoder_sequence}
														_SCREAMING_SNAKE_CASE					=			{0: "batch", 2: encoder_sequence}

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 			UpperCamelCase_				(  A     ):




      '''simple docstring'''







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase=False):
            lowerCAmelCase_			,		lowerCAmelCase_			,		lowerCAmelCase_						=      (
                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()
            lowerCAmelCase_						=      pa.Table.from_pydict({'''filename''': [Path(_UpperCAmelCase).name] * 100})
            if take == -1:
                  lowerCAmelCase_						=      len(_UpperCAmelCase) - skip
            if skip is not None and take is not None:
                  lowerCAmelCase_						=      pa_table.slice(_UpperCAmelCase					,					_UpperCAmelCase)
            return pa_table


class 			UpperCamelCase_				(  A     ):




      '''simple docstring'''







      def lowercase__		( self):
            lowerCAmelCase_						=      '''my_name'''
            lowerCAmelCase_						=      SplitInfo(name='''train'''					,					num_examples=100)
            lowerCAmelCase_						=      SplitInfo(name='''test'''					,					num_examples=100)
            lowerCAmelCase_						=      [train_info, test_info]
            lowerCAmelCase_						=      SplitDict()
            split_dict.add(_UpperCAmelCase)
            split_dict.add(_UpperCAmelCase)
            lowerCAmelCase_						=      DatasetInfo(splits=_UpperCAmelCase)

            with tempfile.TemporaryDirectory() as tmp_dir:
                  lowerCAmelCase_						=      ReaderTest(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      '''test[:33%]'''
                  lowerCAmelCase_						=      Dataset(**reader.read(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase))
                  self.assertEqual(dset['''filename'''][0]					,					f'{name}-test')
                  self.assertEqual(dset.num_rows					,					33)
                  self.assertEqual(dset.num_columns					,					1)

                  lowerCAmelCase_						=      ['''train''', '''test[:33%]''']
                  lowerCAmelCase_						=      [Split.TRAIN, ReadInstruction.from_spec('''test[:33%]''')]
                  for instructions in [instructionsa, instructionsa]:
                        lowerCAmelCase_						=      [reader.read(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase) for instr in instructions]
                        lowerCAmelCase_			,		lowerCAmelCase_						=      (Dataset(**_UpperCAmelCase) for dataset_kwargs in datasets_kwargs)
                        self.assertEqual(train_dset['''filename'''][0]					,					f'{name}-train')
                        self.assertEqual(train_dset.num_rows					,					100)
                        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					,					33)
                        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 lowercase__		( self):
            lowerCAmelCase_						=      '''my_name'''
            lowerCAmelCase_						=      SplitInfo(name='''train'''					,					num_examples=1_000					,					shard_lengths=[100] * 10)
            lowerCAmelCase_						=      [train_info]
            lowerCAmelCase_						=      SplitDict()
            split_dict.add(_UpperCAmelCase)
            lowerCAmelCase_						=      DatasetInfo(splits=_UpperCAmelCase)

            with tempfile.TemporaryDirectory() as tmp_dir:
                  lowerCAmelCase_						=      ReaderTest(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      '''train[:33%]'''
                  lowerCAmelCase_						=      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					,					330)
                  self.assertEqual(dset.num_columns					,					1)







      def lowercase__		( self):
            lowerCAmelCase_						=      SplitInfo(name='''train'''					,					num_examples=100)
            lowerCAmelCase_						=      SplitInfo(name='''test'''					,					num_examples=100)
            lowerCAmelCase_						=      SplitDict()
            split_dict.add(_UpperCAmelCase)
            split_dict.add(_UpperCAmelCase)
            lowerCAmelCase_						=      DatasetInfo(splits=_UpperCAmelCase)

            with tempfile.TemporaryDirectory() as tmp_dir:
                  lowerCAmelCase_						=      ReaderTest(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      [
                      {'''filename''': os.path.join(_UpperCAmelCase					,					'''train''')},
                      {'''filename''': os.path.join(_UpperCAmelCase					,					'''test'''), '''skip''': 10, '''take''': 10},
                  ]
                  lowerCAmelCase_						=      Dataset(**reader.read_files(_UpperCAmelCase					,					original_instructions='''train+test[10:20]'''))
                  self.assertEqual(dset.num_rows					,					110)
                  self.assertEqual(dset.num_columns					,					1)
                  del dset
@pytest.mark.parametrize('''in_memory'''		,					[False, True]						)
def lowerCamelCase_       (			A			:      List[Any]		,					A			:      Union[str, Any]		,					A			:      Optional[int]						):




      """simple docstring"""
      lowerCAmelCase_						=      arrow_file
      with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
            lowerCAmelCase_						=      ArrowReader.read_table(A		,					in_memory=A						)
      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_       (			A			:      List[str]		,					A			:      List[str]		,					A			:      Dict						):




      """simple docstring"""
      lowerCAmelCase_						=      arrow_file
      lowerCAmelCase_						=      ArrowReader(''''''		,					A						)
      with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
            lowerCAmelCase_						=      reader.read_files([{'''filename''': filename}]		,					in_memory=A						)
      assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
      lowerCAmelCase_						=      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_       (			):




      """simple docstring"""
      assert ReadInstruction('''train'''		,					to=10		,					unit='''abs'''						).to_spec() == "train[:10]"
      assert ReadInstruction('''train'''		,					from_=-80		,					to=10		,					unit='''%'''						).to_spec() == "train[-80%:10%]"

      lowerCAmelCase_						=      '''train+test'''
      assert ReadInstruction.from_spec(A						).to_spec() == spec_train_test

      lowerCAmelCase_						=      '''train[2:10]'''
      assert ReadInstruction.from_spec(A						).to_spec() == spec_train_abs

      lowerCAmelCase_						=      '''train[15%:-20%]'''
      assert ReadInstruction.from_spec(A						).to_spec() == spec_train_pct

      lowerCAmelCase_						=      '''train[:10%](closest)'''
      assert ReadInstruction.from_spec(A						).to_spec() == "train[:10%]"

      lowerCAmelCase_						=      '''train[:10%](pct1_dropremainder)'''
      assert ReadInstruction.from_spec(A						).to_spec() == spec_train_pct_rounding

      lowerCAmelCase_						=      '''train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)'''
      assert ReadInstruction.from_spec(A						).to_spec() == spec_train_test_pct_rounding
def lowerCamelCase_       (			):




      """simple docstring"""
      lowerCAmelCase_						=      '''dummy'''
      lowerCAmelCase_						=      [SplitInfo(name='''train'''		,					num_examples=1_00						)]
      lowerCAmelCase_						=      '''train[:33%]'''
      lowerCAmelCase_						=      '''arrow'''
      lowerCAmelCase_						=      '''prefix'''

      lowerCAmelCase_						=      make_file_instructions(A		,					A		,					A		,					A		,					A						)
      assert isinstance(A		,					A						)
      assert file_instructions.num_examples == 33
      assert file_instructions.file_instructions == [
          {"filename": os.path.join(A		,					F'{name}-train.arrow'						), "skip": 0, "take": 33}
      ]

      lowerCAmelCase_						=      [SplitInfo(name='''train'''		,					num_examples=1_00		,					shard_lengths=[10] * 10						)]
      lowerCAmelCase_						=      make_file_instructions(A		,					A		,					A		,					A		,					A						)
      assert isinstance(A		,					A						)
      assert file_instructions.num_examples == 33
      assert file_instructions.file_instructions == [
          {"filename": os.path.join(A		,					F'{name}-train-00000-of-00010.arrow'						), "skip": 0, "take": -1},
          {"filename": os.path.join(A		,					F'{name}-train-00001-of-00010.arrow'						), "skip": 0, "take": -1},
          {"filename": os.path.join(A		,					F'{name}-train-00002-of-00010.arrow'						), "skip": 0, "take": -1},
          {"filename": os.path.join(A		,					F'{name}-train-00003-of-00010.arrow'						), "skip": 0, "take": 3},
      ]




@pytest.mark.parametrize('''name, expected_exception'''		,					[(None, TypeError), ('''''', ValueError)]						)
def lowerCamelCase_       (			A			:      Any		,					A			:      int						):




      """simple docstring"""
      lowerCAmelCase_						=      [SplitInfo(name='''train'''		,					num_examples=1_00						)]
      lowerCAmelCase_						=      '''train'''
      lowerCAmelCase_						=      '''arrow'''
      lowerCAmelCase_						=      '''prefix_path'''
      with pytest.raises(A						):
            lowerCAmelCase_						=      make_file_instructions(A		,					A		,					A		,					A		,					A						)



