



import collections
import inspect
import unittest

from transformers import DonutSwinConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import DonutSwinModel
	from transformers.models.donut.modeling_donut_swin import DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST





class    __lowerCAmelCase					:




				def __init__(				self		,							_snake_case		,							_snake_case=13		,							_snake_case=32		,							_snake_case=2		,							_snake_case=3		,							_snake_case=16		,							_snake_case=[1, 2, 1]		,							_snake_case=[2, 2, 4]		,							_snake_case=2		,							_snake_case=2.0		,							_snake_case=True		,							_snake_case=0.0		,							_snake_case=0.0		,							_snake_case=0.1		,							_snake_case="gelu"		,							_snake_case=False		,							_snake_case=True		,							_snake_case=0.02		,							_snake_case=1e-5		,							_snake_case=True		,							_snake_case=None		,							_snake_case=True		,							_snake_case=10		,							_snake_case=8		,							):



									"""simple docstring"""





									_lowerCAmelCase         =       parent
									_lowerCAmelCase         =       batch_size
									_lowerCAmelCase         =       image_size
									_lowerCAmelCase         =       patch_size
									_lowerCAmelCase         =       num_channels
									_lowerCAmelCase         =       embed_dim
									_lowerCAmelCase         =       depths
									_lowerCAmelCase         =       num_heads
									_lowerCAmelCase         =       window_size
									_lowerCAmelCase         =       mlp_ratio
									_lowerCAmelCase         =       qkv_bias
									_lowerCAmelCase         =       hidden_dropout_prob
									_lowerCAmelCase         =       attention_probs_dropout_prob
									_lowerCAmelCase         =       drop_path_rate
									_lowerCAmelCase         =       hidden_act
									_lowerCAmelCase         =       use_absolute_embeddings
									_lowerCAmelCase         =       patch_norm
									_lowerCAmelCase         =       layer_norm_eps
									_lowerCAmelCase         =       initializer_range
									_lowerCAmelCase         =       is_training
									_lowerCAmelCase         =       scope
									_lowerCAmelCase         =       use_labels
									_lowerCAmelCase         =       type_sequence_label_size
									_lowerCAmelCase         =       encoder_stride




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

									_lowerCAmelCase         =       None
									if self.use_labels:
														_lowerCAmelCase         =       ids_tensor([self.batch_size]		,							self.type_sequence_label_size						)

									_lowerCAmelCase         =       self.get_config()

									return config, pixel_values, labels




				def 				snake_case  (				self						):



									"""simple docstring"""





									return DonutSwinConfig(
									    image_size=self.image_size		,							patch_size=self.patch_size		,							num_channels=self.num_channels		,							embed_dim=self.embed_dim		,							depths=self.depths		,							num_heads=self.num_heads		,							window_size=self.window_size		,							mlp_ratio=self.mlp_ratio		,							qkv_bias=self.qkv_bias		,							hidden_dropout_prob=self.hidden_dropout_prob		,							attention_probs_dropout_prob=self.attention_probs_dropout_prob		,							drop_path_rate=self.drop_path_rate		,							hidden_act=self.hidden_act		,							use_absolute_embeddings=self.use_absolute_embeddings		,							path_norm=self.patch_norm		,							layer_norm_eps=self.layer_norm_eps		,							initializer_range=self.initializer_range		,							encoder_stride=self.encoder_stride		,							)




				def 				snake_case  (				self		,							_snake_case		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       DonutSwinModel(config=_snake_case						)
									model.to(_snake_case						)
									model.eval()
									_lowerCAmelCase         =       model(_snake_case						)

									_lowerCAmelCase         =       ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
									_lowerCAmelCase         =       int(config.embed_dim * 2 ** (len(config.depths						) - 1)						)

									self.parent.assertEqual(result.last_hidden_state.shape		,							(self.batch_size, expected_seq_len, expected_dim)						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.prepare_config_and_inputs()
									(
									    (
									    _lowerCAmelCase
									)      ,      (
									    _lowerCAmelCase
									)      ,      (
									    _lowerCAmelCase
									)      ,      
									)         =       config_and_inputs
									_lowerCAmelCase         =       {"""pixel_values""": pixel_values}
									return config, inputs_dict





@require_torch
class    __lowerCAmelCase					(	lowerCamelCase__			,		lowerCamelCase__			,		unittest.TestCase			):
				__lowerCamelCase					=							(DonutSwinModel,) if is_torch_available() else ()
				__lowerCamelCase					=							{'''feature-extraction''': DonutSwinModel} if is_torch_available() else {}
				__lowerCamelCase					=							True

				__lowerCamelCase					=							False
				__lowerCamelCase					=							False
				__lowerCamelCase					=							False




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       DonutSwinModelTester(self						)
									_lowerCAmelCase         =       ConfigTester(self		,							config_class=_snake_case		,							embed_dim=37						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									self.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()




				def 				snake_case  (				self						):



									"""simple docstring"""





									return




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_snake_case						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									pass




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase      ,      _lowerCAmelCase         =       self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														_lowerCAmelCase         =       model_class(_snake_case						)
														self.assertIsInstance(model.get_input_embeddings()		,							(nn.Module)						)
														_lowerCAmelCase         =       model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(_snake_case		,							nn.Linear						)						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase      ,      _lowerCAmelCase         =       self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														_lowerCAmelCase         =       model_class(_snake_case						)
														_lowerCAmelCase         =       inspect.signature(model.forward						)
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														_lowerCAmelCase         =       [*signature.parameters.keys()]

														_lowerCAmelCase         =       ["""pixel_values"""]
														self.assertListEqual(arg_names[:1]		,							_snake_case						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase      ,      _lowerCAmelCase         =       self.model_tester.prepare_config_and_inputs_for_common()
									_lowerCAmelCase         =       True

									for model_class in self.all_model_classes:
														_lowerCAmelCase         =       True
														_lowerCAmelCase         =       False
														_lowerCAmelCase         =       True
														_lowerCAmelCase         =       model_class(_snake_case						)
														model.to(_snake_case						)
														model.eval()
														with torch.no_grad():
																			_lowerCAmelCase         =       model(**self._prepare_for_class(_snake_case		,							_snake_case						)						)
														_lowerCAmelCase         =       outputs.attentions
														_lowerCAmelCase         =       len(self.model_tester.depths						)
														self.assertEqual(len(_snake_case						)		,							_snake_case						)

														# check that output_attentions also work using config
														del inputs_dict["output_attentions"]
														_lowerCAmelCase         =       True
														_lowerCAmelCase         =       config.window_size**2
														_lowerCAmelCase         =       model_class(_snake_case						)
														model.to(_snake_case						)
														model.eval()
														with torch.no_grad():
																			_lowerCAmelCase         =       model(**self._prepare_for_class(_snake_case		,							_snake_case						)						)
														_lowerCAmelCase         =       outputs.attentions
														self.assertEqual(len(_snake_case						)		,							_snake_case						)

														self.assertListEqual(
														    list(attentions[0].shape[-3:]						)		,							[self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,							)
														_lowerCAmelCase         =       len(_snake_case						)

														# Check attention is always last and order is fine
														_lowerCAmelCase         =       True
														_lowerCAmelCase         =       True
														_lowerCAmelCase         =       model_class(_snake_case						)
														model.to(_snake_case						)
														model.eval()
														with torch.no_grad():
																			_lowerCAmelCase         =       model(**self._prepare_for_class(_snake_case		,							_snake_case						)						)

														if hasattr(self.model_tester		,							"""num_hidden_states_types"""						):
																			_lowerCAmelCase         =       self.model_tester.num_hidden_states_types
														else:
																			# also another +1 for reshaped_hidden_states
																			_lowerCAmelCase         =       2
														self.assertEqual(out_len + added_hidden_states		,							len(_snake_case						)						)

														_lowerCAmelCase         =       outputs.attentions

														self.assertEqual(len(_snake_case						)		,							_snake_case						)

														self.assertListEqual(
														    list(self_attentions[0].shape[-3:]						)		,							[self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,							)




				def 				snake_case  (				self		,							_snake_case		,							_snake_case		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       model_class(_snake_case						)
									model.to(_snake_case						)
									model.eval()

									with torch.no_grad():
														_lowerCAmelCase         =       model(**self._prepare_for_class(_snake_case		,							_snake_case						)						)

									_lowerCAmelCase         =       outputs.hidden_states

									_lowerCAmelCase         =       getattr(
									    self.model_tester		,							"""expected_num_hidden_layers"""		,							len(self.model_tester.depths						) + 1						)
									self.assertEqual(len(_snake_case						)		,							_snake_case						)

									# DonutSwin has a different seq_length
									_lowerCAmelCase         =       (
									    config.patch_size
									    if isinstance(config.patch_size		,							collections.abc.Iterable						)
									    else (config.patch_size, config.patch_size)
									)

									_lowerCAmelCase         =       (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]						)		,							[num_patches, self.model_tester.embed_dim]		,							)

									_lowerCAmelCase         =       outputs.reshaped_hidden_states
									self.assertEqual(len(_snake_case						)		,							_snake_case						)

									_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase         =       reshaped_hidden_states[0].shape
									_lowerCAmelCase         =       (
									    reshaped_hidden_states[0].view(_snake_case		,							_snake_case		,							height * width						).permute(0		,							2		,							1						)
									)
									self.assertListEqual(
									    list(reshaped_hidden_states.shape[-2:]						)		,							[num_patches, self.model_tester.embed_dim]		,							)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase      ,      _lowerCAmelCase         =       self.model_tester.prepare_config_and_inputs_for_common()

									_lowerCAmelCase         =       (
									    self.model_tester.image_size
									    if isinstance(self.model_tester.image_size		,							collections.abc.Iterable						)
									    else (self.model_tester.image_size, self.model_tester.image_size)
									)

									for model_class in self.all_model_classes:
														_lowerCAmelCase         =       True
														self.check_hidden_states_output(_snake_case		,							_snake_case		,							_snake_case		,							_snake_case						)

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														_lowerCAmelCase         =       True

														self.check_hidden_states_output(_snake_case		,							_snake_case		,							_snake_case		,							_snake_case						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase      ,      _lowerCAmelCase         =       self.model_tester.prepare_config_and_inputs_for_common()
									_lowerCAmelCase         =       3

									_lowerCAmelCase         =       (
									    self.model_tester.image_size
									    if isinstance(self.model_tester.image_size		,							collections.abc.Iterable						)
									    else (self.model_tester.image_size, self.model_tester.image_size)
									)
									_lowerCAmelCase         =       (
									    config.patch_size
									    if isinstance(config.patch_size		,							collections.abc.Iterable						)
									    else (config.patch_size, config.patch_size)
									)

									_lowerCAmelCase         =       image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
									_lowerCAmelCase         =       image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

									for model_class in self.all_model_classes:
														_lowerCAmelCase         =       True
														self.check_hidden_states_output(_snake_case		,							_snake_case		,							_snake_case		,							(padded_height, padded_width)						)

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														_lowerCAmelCase         =       True
														self.check_hidden_states_output(_snake_case		,							_snake_case		,							_snake_case		,							(padded_height, padded_width)						)




				@slow
				def 				snake_case  (				self						):



									"""simple docstring"""





									for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_lowerCAmelCase         =       DonutSwinModel.from_pretrained(_snake_case						)
														self.assertIsNotNone(_snake_case						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase      ,      _lowerCAmelCase         =       self.model_tester.prepare_config_and_inputs_for_common()

									_lowerCAmelCase         =       _config_zero_init(_snake_case						)
									for model_class in self.all_model_classes:
														_lowerCAmelCase         =       model_class(config=_snake_case						)
														for name, param in model.named_parameters():
																			if "embeddings" not in name and param.requires_grad:
																								self.assertIn(
																								    ((param.data.mean() * 1e9).round() / 1e9).item()		,							[0.0, 1.0]		,							msg=F'Parameter {name} of model {model_class} seems not properly initialized'		,							)





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						)



