
import unittest

from transformers import MarkupLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property

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


if is_torch_available():
		import torch

		from transformers import (
		    MarkupLMForQuestionAnswering,
		    MarkupLMForSequenceClassification,
		    MarkupLMForTokenClassification,
		    MarkupLMModel,
		)

# TODO check dependencies
from transformers import MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer


class 						__magic_name__				:





		def __init__(							self      :		List[Any]  ,					lowerCamelCase__      :		Optional[int]  ,					lowerCamelCase__      :		str=1_3  ,					lowerCamelCase__      :		List[Any]=7  ,					lowerCamelCase__      :		Optional[Any]=True  ,					lowerCamelCase__      :		List[Any]=True  ,					lowerCamelCase__      :		Optional[int]=True  ,					lowerCamelCase__      :		str=True  ,					lowerCamelCase__      :		Optional[Any]=9_9  ,					lowerCamelCase__      :		Optional[Any]=3_2  ,					lowerCamelCase__      :		Tuple=5  ,					lowerCamelCase__      :		str=4  ,					lowerCamelCase__      :		str=3_7  ,					lowerCamelCase__      :		Optional[int]="gelu"  ,					lowerCamelCase__      :		Union[str, Any]=0.1  ,					lowerCamelCase__      :		List[Any]=0.1  ,					lowerCamelCase__      :		str=5_1_2  ,					lowerCamelCase__      :		Optional[int]=1_6  ,					lowerCamelCase__      :		Dict=2  ,					lowerCamelCase__      :		Optional[Any]=0.0_2  ,					lowerCamelCase__      :		int=3  ,					lowerCamelCase__      :		List[str]=None  ,					lowerCamelCase__      :		Optional[Any]=2_0  ,					lowerCamelCase__      :		Any=3_0  ,					lowerCamelCase__      :		Optional[Any]=2  ,					lowerCamelCase__      :		List[str]=2  ,					lowerCamelCase__      :		Any=1_0  ,					):
							lowerCAmelCase						:   int							=       parent
							lowerCAmelCase						:   Tuple							=       batch_size
							lowerCAmelCase						:   Optional[Any]							=       seq_length
							lowerCAmelCase						:   List[Any]							=       is_training
							lowerCAmelCase						:   str							=       use_input_mask
							lowerCAmelCase						:   Optional[Any]							=       use_token_type_ids
							lowerCAmelCase						:   List[str]							=       use_labels
							lowerCAmelCase						:   Optional[int]							=       vocab_size
							lowerCAmelCase						:   Optional[int]							=       hidden_size
							lowerCAmelCase						:   Tuple							=       num_hidden_layers
							lowerCAmelCase						:   Any							=       num_attention_heads
							lowerCAmelCase						:   int							=       intermediate_size
							lowerCAmelCase						:   Union[str, Any]							=       hidden_act
							lowerCAmelCase						:   str							=       hidden_dropout_prob
							lowerCAmelCase						:   Tuple							=       attention_probs_dropout_prob
							lowerCAmelCase						:   str							=       max_position_embeddings
							lowerCAmelCase						:   List[Any]							=       type_vocab_size
							lowerCAmelCase						:   Optional[Any]							=       type_sequence_label_size
							lowerCAmelCase						:   List[str]							=       initializer_range
							lowerCAmelCase						:   List[Any]							=       num_labels
							lowerCAmelCase						:   Tuple							=       scope
							lowerCAmelCase						:   Any							=       max_xpath_tag_unit_embeddings
							lowerCAmelCase						:   Dict							=       max_xpath_subs_unit_embeddings
							lowerCAmelCase						:   Tuple							=       tag_pad_id
							lowerCAmelCase						:   Any							=       subs_pad_id
							lowerCAmelCase						:   int							=       max_depth





		def  _A							(							self      :		List[str]			):
							lowerCAmelCase						:   Tuple							=       ids_tensor([self.batch_size, self.seq_length]  ,					self.vocab_size			)

							lowerCAmelCase						:   int							=       ids_tensor(
							    [self.batch_size, self.seq_length, self.max_depth]  ,					self.max_xpath_tag_unit_embeddings			)

							lowerCAmelCase						:   Optional[int]							=       ids_tensor(
							    [self.batch_size, self.seq_length, self.max_depth]  ,					self.max_xpath_subs_unit_embeddings			)

							lowerCAmelCase						:   str							=       None
							if self.use_input_mask:
												lowerCAmelCase						:   str							=       ids_tensor([self.batch_size, self.seq_length]  ,					vocab_size=2			)

							lowerCAmelCase						:   Optional[int]							=       None
							if self.use_token_type_ids:
												lowerCAmelCase						:   Optional[Any]							=       ids_tensor([self.batch_size, self.seq_length]  ,					self.type_vocab_size			)

							lowerCAmelCase						:   Optional[Any]							=       None
							lowerCAmelCase						:   str							=       None
							if self.use_labels:
												lowerCAmelCase						:   Optional[int]							=       ids_tensor([self.batch_size]  ,					self.type_sequence_label_size			)
												lowerCAmelCase						:   Optional[Any]							=       ids_tensor([self.batch_size, self.seq_length]  ,					self.num_labels			)

							lowerCAmelCase						:   Tuple							=       self.get_config()

							return (
							    config,
							    input_ids,
							    xpath_tags_seq,
							    xpath_subs_seq,
							    token_type_ids,
							    input_mask,
							    sequence_labels,
							    token_labels,
							)





		def  _A							(							self      :		Dict			):
							return MarkupLMConfig(
							    vocab_size=self.vocab_size  ,					hidden_size=self.hidden_size  ,					num_hidden_layers=self.num_hidden_layers  ,					num_attention_heads=self.num_attention_heads  ,					intermediate_size=self.intermediate_size  ,					hidden_act=self.hidden_act  ,					hidden_dropout_prob=self.hidden_dropout_prob  ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,					max_position_embeddings=self.max_position_embeddings  ,					type_vocab_size=self.type_vocab_size  ,					initializer_range=self.initializer_range  ,					max_xpath_tag_unit_embeddings=self.max_xpath_tag_unit_embeddings  ,					max_xpath_subs_unit_embeddings=self.max_xpath_subs_unit_embeddings  ,					tag_pad_id=self.tag_pad_id  ,					subs_pad_id=self.subs_pad_id  ,					max_depth=self.max_depth  ,					)





		def  _A							(							self      :		Union[str, Any]  ,					lowerCamelCase__      :		Optional[int]  ,					lowerCamelCase__      :		Union[str, Any]  ,					lowerCamelCase__      :		List[str]  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		Tuple  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		int  ,					lowerCamelCase__      :		List[str]  ,					):
							lowerCAmelCase						:   List[str]							=       MarkupLMModel(config=lowerCamelCase__			)
							model.to(lowerCamelCase__			)
							model.eval()
							print('''Configs:'''  ,					model.config.tag_pad_id  ,					model.config.subs_pad_id			)
							lowerCAmelCase						:   Dict							=       model(lowerCamelCase__  ,					attention_mask=lowerCamelCase__  ,					token_type_ids=lowerCamelCase__			)
							lowerCAmelCase						:   Union[str, Any]							=       model(lowerCamelCase__  ,					token_type_ids=lowerCamelCase__			)
							lowerCAmelCase						:   Optional[Any]							=       model(lowerCamelCase__			)
							self.parent.assertEqual(result.last_hidden_state.shape  ,					(self.batch_size, self.seq_length, self.hidden_size)			)
							self.parent.assertEqual(result.pooler_output.shape  ,					(self.batch_size, self.hidden_size)			)





		def  _A							(							self      :		Tuple  ,					lowerCamelCase__      :		Optional[int]  ,					lowerCamelCase__      :		Dict  ,					lowerCamelCase__      :		Union[str, Any]  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		List[str]  ,					lowerCamelCase__      :		List[Any]  ,					lowerCamelCase__      :		List[Any]  ,					lowerCamelCase__      :		Dict  ,					):
							lowerCAmelCase						:   str							=       self.num_labels
							lowerCAmelCase						:   Union[str, Any]							=       MarkupLMForSequenceClassification(lowerCamelCase__			)
							model.to(lowerCamelCase__			)
							model.eval()
							lowerCAmelCase						:   Optional[Any]							=       model(
							    lowerCamelCase__  ,					xpath_tags_seq=lowerCamelCase__  ,					xpath_subs_seq=lowerCamelCase__  ,					attention_mask=lowerCamelCase__  ,					token_type_ids=lowerCamelCase__  ,					labels=lowerCamelCase__  ,					)
							self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.num_labels)			)





		def  _A							(							self      :		Union[str, Any]  ,					lowerCamelCase__      :		Optional[int]  ,					lowerCamelCase__      :		List[str]  ,					lowerCamelCase__      :		List[str]  ,					lowerCamelCase__      :		Optional[Any]  ,					lowerCamelCase__      :		int  ,					lowerCamelCase__      :		Tuple  ,					lowerCamelCase__      :		Tuple  ,					lowerCamelCase__      :		int  ,					):
							lowerCAmelCase						:   List[str]							=       self.num_labels
							lowerCAmelCase						:   Union[str, Any]							=       MarkupLMForTokenClassification(config=lowerCamelCase__			)
							model.to(lowerCamelCase__			)
							model.eval()
							lowerCAmelCase						:   int							=       model(
							    lowerCamelCase__  ,					xpath_tags_seq=lowerCamelCase__  ,					xpath_subs_seq=lowerCamelCase__  ,					attention_mask=lowerCamelCase__  ,					token_type_ids=lowerCamelCase__  ,					labels=lowerCamelCase__  ,					)
							self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.seq_length, self.num_labels)			)





		def  _A							(							self      :		int  ,					lowerCamelCase__      :		Union[str, Any]  ,					lowerCamelCase__      :		List[str]  ,					lowerCamelCase__      :		Optional[int]  ,					lowerCamelCase__      :		Tuple  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		Union[str, Any]  ,					lowerCamelCase__      :		List[str]  ,					lowerCamelCase__      :		Optional[int]  ,					):
							lowerCAmelCase						:   Optional[int]							=       MarkupLMForQuestionAnswering(config=lowerCamelCase__			)
							model.to(lowerCamelCase__			)
							model.eval()
							lowerCAmelCase						:   Optional[Any]							=       model(
							    lowerCamelCase__  ,					xpath_tags_seq=lowerCamelCase__  ,					xpath_subs_seq=lowerCamelCase__  ,					attention_mask=lowerCamelCase__  ,					token_type_ids=lowerCamelCase__  ,					start_positions=lowerCamelCase__  ,					end_positions=lowerCamelCase__  ,					)
							self.parent.assertEqual(result.start_logits.shape  ,					(self.batch_size, self.seq_length)			)
							self.parent.assertEqual(result.end_logits.shape  ,					(self.batch_size, self.seq_length)			)





		def  _A							(							self      :		Optional[int]			):
							lowerCAmelCase						:   Dict							=       self.prepare_config_and_inputs()
							(
							    lowerCAmelCase							
							)						:   Dict							=       config_and_inputs
							lowerCAmelCase						:   List[Any]							=       {
							    '''input_ids''': input_ids,
							    '''xpath_tags_seq''': xpath_tags_seq,
							    '''xpath_subs_seq''': xpath_subs_seq,
							    '''token_type_ids''': token_type_ids,
							    '''attention_mask''': input_mask,
							}
							return config, inputs_dict



@require_torch
class 						__magic_name__				(  snake_case,			snake_case,			unittest.TestCase      ):
		_lowerCAmelCase						=		(
		    (
		        MarkupLMModel,
		        MarkupLMForSequenceClassification,
		        MarkupLMForTokenClassification,
		        MarkupLMForQuestionAnswering,
		    )
		    if is_torch_available()
		    else None
		)
		_lowerCAmelCase						=		(
		    {
		        "feature-extraction": MarkupLMModel,
		        "question-answering": MarkupLMForQuestionAnswering,
		        "text-classification": MarkupLMForSequenceClassification,
		        "token-classification": MarkupLMForTokenClassification,
		        "zero-shot": MarkupLMForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)





		def  _A							(							self      :		Optional[int]  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		Any  ,					lowerCamelCase__      :		List[str]  ,					lowerCamelCase__      :		List[Any]  ,					lowerCamelCase__      :		Any			):
							# ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
							# (batch of pretokenized examples).
							return True





		def  _A							(							self      :		Union[str, Any]			):
							lowerCAmelCase						:   Tuple							=       MarkupLMModelTester(self			)
							lowerCAmelCase						:   Dict							=       ConfigTester(self  ,					config_class=lowerCamelCase__  ,					hidden_size=3_7			)





		def  _A							(							self      :		Any			):
							self.config_tester.run_common_tests()





		def  _A							(							self      :		Optional[Any]			):
							lowerCAmelCase						:   Tuple							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCamelCase__			)





		def  _A							(							self      :		Optional[int]			):
							lowerCAmelCase						:   Any							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__			)





		def  _A							(							self      :		int			):
							lowerCAmelCase						:   Optional[Any]							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__			)





		def  _A							(							self      :		List[str]			):
							lowerCAmelCase						:   Dict							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__			)



def       UpperCAmelCase__							(    ):


					'''simple docstring'''

					lowerCAmelCase						:   int							=       '''
    <!DOCTYPE html>
    <html>
    <head>
    <title>Page Title</title>
    </head>
    <body>

    <h1>This is a Heading</h1>
    <p>This is a paragraph.</p>

    </body>
    </html>
    '''

					return html_string


@require_torch
class 						__magic_name__				(  unittest.TestCase      ):





		@cached_property
		def  _A							(							self      :		Optional[int]			):
							# TODO use from_pretrained here
							lowerCAmelCase						:   List[Any]							=       MarkupLMFeatureExtractor()
							lowerCAmelCase						:   List[str]							=       MarkupLMTokenizer.from_pretrained('''microsoft/markuplm-base'''			)

							return MarkupLMProcessor(lowerCamelCase__  ,					lowerCamelCase__			)





		@slow
		def  _A							(							self      :		Optional[int]			):
							lowerCAmelCase						:   str							=       MarkupLMModel.from_pretrained('''microsoft/markuplm-base'''			).to(lowerCamelCase__			)

							lowerCAmelCase						:   List[Any]							=       self.default_processor

							lowerCAmelCase						:   Tuple							=       processor(prepare_html_string()  ,					return_tensors='''pt'''			)
							lowerCAmelCase						:   Optional[Any]							=       inputs.to(lowerCamelCase__			)

							# forward pass
							with torch.no_grad():
												lowerCAmelCase						:   str							=       model(**lowerCamelCase__			)

							# verify the last hidden states
							lowerCAmelCase						:   Dict							=       torch.Size([1, 1_4, 7_6_8]			)
							self.assertEqual(outputs.last_hidden_state.shape  ,					lowerCamelCase__			)

							lowerCAmelCase						:   Dict							=       torch.tensor(
							    [[0.0_6_7_5, -0.0_0_5_2, 0.5_0_0_1], [-0.2_2_8_1, 0.0_8_0_2, 0.2_1_9_2], [-0.0_5_8_3, -0.3_3_1_1, 0.1_1_8_5]]			).to(lowerCamelCase__			)

							self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]  ,					lowerCamelCase__  ,					atol=1E-4			)			)


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 						__magic_name__				(  snake_case      ):





		def  _A							(							self      :		Any  ,					lowerCamelCase__      :		Any  ,					lowerCamelCase__      :		List[Any]=False			):
							lowerCAmelCase		,							lowerCAmelCase		,							lowerCAmelCase						:   List[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()
							lowerCAmelCase						:   int							=       pa.Table.from_pydict({'''filename''': [Path(lowerCamelCase__			).name] * 1_0_0}			)
							if take == -1:
												lowerCAmelCase						:   Optional[int]							=       len(lowerCamelCase__			) - skip
							if skip is not None and take is not None:
												lowerCAmelCase						:   List[str]							=       pa_table.slice(lowerCamelCase__  ,					lowerCamelCase__			)
							return pa_table



class 						__magic_name__				(  snake_case      ):





		def  _A							(							self      :		Union[str, Any]			):
							lowerCAmelCase						:   List[str]							=       '''my_name'''
							lowerCAmelCase						:   List[str]							=       SplitInfo(name='''train'''  ,					num_examples=1_0_0			)
							lowerCAmelCase						:   str							=       SplitInfo(name='''test'''  ,					num_examples=1_0_0			)
							lowerCAmelCase						:   int							=       [train_info, test_info]
							lowerCAmelCase						:   Union[str, Any]							=       SplitDict()
							split_dict.add(lowerCamelCase__			)
							split_dict.add(lowerCamelCase__			)
							lowerCAmelCase						:   Union[str, Any]							=       DatasetInfo(splits=lowerCamelCase__			)

							with tempfile.TemporaryDirectory() as tmp_dir:
												lowerCAmelCase						:   Tuple							=       ReaderTest(lowerCamelCase__  ,					lowerCamelCase__			)

												lowerCAmelCase						:   int							=       '''test[:33%]'''
												lowerCAmelCase						:   Dict							=       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			)

												lowerCAmelCase						:   str							=       ['''train''', '''test[:33%]''']
												lowerCAmelCase						:   Tuple							=       [Split.TRAIN, ReadInstruction.from_spec('''test[:33%]'''			)]
												for instructions in [instructionsa, instructionsa]:
																	lowerCAmelCase						:   Optional[Any]							=       [reader.read(lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__			) for instr in instructions]
																	lowerCAmelCase		,							lowerCAmelCase						:   Tuple							=       (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  _A							(							self      :		str			):
							lowerCAmelCase						:   Optional[Any]							=       '''my_name'''
							lowerCAmelCase						:   List[Any]							=       SplitInfo(name='''train'''  ,					num_examples=1_0_0_0  ,					shard_lengths=[1_0_0] * 1_0			)
							lowerCAmelCase						:   Optional[int]							=       [train_info]
							lowerCAmelCase						:   Union[str, Any]							=       SplitDict()
							split_dict.add(lowerCamelCase__			)
							lowerCAmelCase						:   Optional[Any]							=       DatasetInfo(splits=lowerCamelCase__			)

							with tempfile.TemporaryDirectory() as tmp_dir:
												lowerCAmelCase						:   Tuple							=       ReaderTest(lowerCamelCase__  ,					lowerCamelCase__			)

												lowerCAmelCase						:   Dict							=       '''train[:33%]'''
												lowerCAmelCase						:   int							=       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  _A							(							self      :		Union[str, Any]			):
							lowerCAmelCase						:   Union[str, Any]							=       SplitInfo(name='''train'''  ,					num_examples=1_0_0			)
							lowerCAmelCase						:   int							=       SplitInfo(name='''test'''  ,					num_examples=1_0_0			)
							lowerCAmelCase						:   Optional[Any]							=       SplitDict()
							split_dict.add(lowerCamelCase__			)
							split_dict.add(lowerCamelCase__			)
							lowerCAmelCase						:   List[Any]							=       DatasetInfo(splits=lowerCamelCase__			)

							with tempfile.TemporaryDirectory() as tmp_dir:
												lowerCAmelCase						:   Optional[int]							=       ReaderTest(lowerCamelCase__  ,					lowerCamelCase__			)

												lowerCAmelCase						:   int							=       [
												    {'''filename''': os.path.join(lowerCamelCase__  ,					'''train'''			)},
												    {'''filename''': os.path.join(lowerCamelCase__  ,					'''test'''			), '''skip''': 1_0, '''take''': 1_0},
												]
												lowerCAmelCase						:   Optional[int]							=       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       UpperCAmelCase__							(    __magic_name__  :       Any							,					__magic_name__  :       Dict							,					__magic_name__  :       List[str]					):


					'''simple docstring'''

					lowerCAmelCase						:   Dict							=       arrow_file
					with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
										lowerCAmelCase						:   Any							=       ArrowReader.read_table(__magic_name__							,					in_memory=__magic_name__					)
					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__							(    __magic_name__  :       Union[str, Any]							,					__magic_name__  :       List[Any]							,					__magic_name__  :       Dict					):


					'''simple docstring'''

					lowerCAmelCase						:   Any							=       arrow_file
					lowerCAmelCase						:   Union[str, Any]							=       ArrowReader(''''''							,					__magic_name__					)
					with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
										lowerCAmelCase						:   Union[str, Any]							=       reader.read_files([{'''filename''': filename}]							,					in_memory=__magic_name__					)
					assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
					lowerCAmelCase						:   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       UpperCAmelCase__							(    ):


					'''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						:   Tuple							=       '''train+test'''
					assert ReadInstruction.from_spec(__magic_name__					).to_spec() == spec_train_test

					lowerCAmelCase						:   List[Any]							=       '''train[2:10]'''
					assert ReadInstruction.from_spec(__magic_name__					).to_spec() == spec_train_abs

					lowerCAmelCase						:   Optional[Any]							=       '''train[15%:-20%]'''
					assert ReadInstruction.from_spec(__magic_name__					).to_spec() == spec_train_pct

					lowerCAmelCase						:   Tuple							=       '''train[:10%](closest)'''
					assert ReadInstruction.from_spec(__magic_name__					).to_spec() == "train[:10%]"

					lowerCAmelCase						:   Optional[int]							=       '''train[:10%](pct1_dropremainder)'''
					assert ReadInstruction.from_spec(__magic_name__					).to_spec() == spec_train_pct_rounding

					lowerCAmelCase						:   Optional[int]							=       '''train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)'''
					assert ReadInstruction.from_spec(__magic_name__					).to_spec() == spec_train_test_pct_rounding
def       UpperCAmelCase__							(    ):


					'''simple docstring'''

					lowerCAmelCase						:   List[Any]							=       '''dummy'''
					lowerCAmelCase						:   Optional[Any]							=       [SplitInfo(name='''train'''							,					num_examples=1_00					)]
					lowerCAmelCase						:   Optional[int]							=       '''train[:33%]'''
					lowerCAmelCase						:   Dict							=       '''arrow'''
					lowerCAmelCase						:   Optional[int]							=       '''prefix'''

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

					lowerCAmelCase						:   Dict							=       [SplitInfo(name='''train'''							,					num_examples=1_00							,					shard_lengths=[10] * 10					)]
					lowerCAmelCase						:   str							=       make_file_instructions(__magic_name__							,					__magic_name__							,					__magic_name__							,					__magic_name__							,					__magic_name__					)
					assert isinstance(__magic_name__							,					__magic_name__					)
					assert file_instructions.num_examples == 33
					assert file_instructions.file_instructions == [
					    {"filename": os.path.join(__magic_name__							,					f'''{name}-train-00000-of-00010.arrow'''					), "skip": 0, "take": -1},
					    {"filename": os.path.join(__magic_name__							,					f'''{name}-train-00001-of-00010.arrow'''					), "skip": 0, "take": -1},
					    {"filename": os.path.join(__magic_name__							,					f'''{name}-train-00002-of-00010.arrow'''					), "skip": 0, "take": -1},
					    {"filename": os.path.join(__magic_name__							,					f'''{name}-train-00003-of-00010.arrow'''					), "skip": 0, "take": 3},
					]
@pytest.mark.parametrize('''name, expected_exception'''							,					[(None, TypeError), ('''''', ValueError)]					)
def       UpperCAmelCase__							(    __magic_name__  :       int							,					__magic_name__  :       Optional[int]					):


					'''simple docstring'''

					lowerCAmelCase						:   Any							=       [SplitInfo(name='''train'''							,					num_examples=1_00					)]
					lowerCAmelCase						:   List[Any]							=       '''train'''
					lowerCAmelCase						:   Tuple							=       '''arrow'''
					lowerCAmelCase						:   List[Any]							=       '''prefix_path'''
					with pytest.raises(__magic_name__					):
										lowerCAmelCase						:   Optional[Any]							=       make_file_instructions(__magic_name__							,					__magic_name__							,					__magic_name__							,					__magic_name__							,					__magic_name__					)
